Abstract: So far, most researches on LoRa technology are about single-application oriented IoT, low utilization of configurable parameters leaves room for further optimization of network performance. In order to adapt to the growing transmission requirements of heterogeneous multi-type services, it is increasing essential to optimize the performance of the LoRa network. To address the above issue, a dynamic parameters adaptive configuration strategy based on simulated annealing genetic algorithm is proposed, which can improve the number of end devices and data throughput supported by single gateway LoRa network while limiting energy consumption. The simulation results based on LoRaSim reveal that the proposed method outperforms ADR by 25.6%. By simulating the single gateway LoRa network of nearly over 1000 devices, the experimental results show that when packet generation rate 1/100 s, dynamic parameters adaptive configuration strategy proposed in this study can guarantee PDR above 90%. This method can adapt to the data transmission needs of multi-heterogeneous applications and effectively improve the data throughput while ensuring the PDR of each applications.
Abstract: Detecting malicious URL is important for defending against cyber attacks. In view of the problem that supervised learning requires a large number of labeled samples, this study uses a semi-supervised learning method to train malicious URL detection models, which reduces the cost overhead of labeling data. We propose an improved algorithm based on the traditional co-training. Two kinds of classifiers are trained by using expert knowledge and Doc2Vec pre-processed data, and the data with the same prediction result and the high confidence of the two classifiers are screened and used for classifiers learning after being pseudo-labeled. The experimental results show that the proposed method can train two different types of classifiers with detection precision of 99.42% and 95.23% with only 0.67% of labeled data, which is similar to supervised learning performance and performs better than self-training and co-training.
Abstract: To solve the blank of current research on the prediction of density limit disruption of EAST, 972 density limit disruptive pulses selected as data sets from the EAST’s 2014 to 2019 discharge. 13 diagnostic signals were chosen as features. Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) was used as models and the disruption risk was used as output to build the predictors. The experimental results show that for density limit disruptive pulses, under different alarming times, the successful prediction rate of LSTM (around 95%) is higher than that of MLP (85%), and for non-disruptive pulses, the false prediction rate is around 8% for both MLP and LSTM. The performance of LSTM has great improvement than MLP, shows the feasibility of building EAST density limit disruption system with neural networks and improving the response performance of disruption avoidance and mitigation system.
Abstract: Polarimetric Synthetic Aperture Radar (PolSAR) is a type of microwave imaging radar that avoids the influence of weather, light and clouds, and it has the capability of all-day and all-weather imaging. Therefore, PolSAR images have become one of the main data sources for land classification based on remote sensing image. From the perspective of technical methods, this paper discusses the methods and applications of land classification based on PolSAR image in recent years. It introduces the technical methods and experimental effects, and analyzes the development trend of land classification based on PolSAR image.
Abstract: With the rapid development of modern technology, the data center has become the IT infrastructure of the information society, storing and managing a large amount of key data. At present, the management of data centers mostly relies on experienced professional operation and maintenance personnel to use computers to automatically monitor equipment room equipment indicators and make multiple inspections of equipment, which is time-consuming and tedious. Deep learning and artificial intelligence technologies are currently attracting more and more attention and have achieved many successful applications in the Internet and industrial fields. This study designs a Gated Recurrent Unit (GRU) based deep learning framework to automatically diagnose equipment failures in cloud data center equipment rooms and combines timing information to predict future states based on past equipment operating status information. Series data are split into fixed time windows as input to the bidirectional GRU layer which makes the network learn the time dependency relationship in data points. Besides, we add an attention layer and embedding layer after the output of GRU unit, to help the neural network learning more efficient features for prediction task and further dimension reduction. At last, multi-layer perception is used to classify the data. Experimental results based on real data sets show that proposed neural network framework based on GRU can accurately detect cloud data center faults compared with LSTM, SVM and KNN.
Abstract: Big data industry has risen to the national strategy. The establishment of big data laboratory and experimental curriculum system is necessary for training big data technical personnel. This paper combs the knowledge system of big data, analyzes the training objectives and career orientation of the major of “data science and big data technology” and the major of “big data technology and application”, and clarifies the key knowledge that big data students should master and the professional skills that need to be cultivated, introduces the mainstream big data ecosystem, selects the most general big data architecture, proposes different plans to build big data laboratory in single machine environment, single machine virtualization environment, shared big data cluster environment and cloud computing environment, and designs the big data experiment curriculum system and experiment projects.
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